5 papers
MCMC-Free Uncertainty Quantification for Deep Generative Inference for Spatially Varying Coefficient Models at Scale
Yeseul Jeon, Aaron Scheffler, Rajarshi Guhaniyogi
Varying coefficient (VC) regression models have become indispensable tools in spatial data analysis, providing unmatched flexibility in capturing complex, nonlinear relationships a…
Marginally Correct Tool Caches Can Reverse Group-Normalized Policy Updates
Shivam Gupta
Tool-result caching reduces repeated execution in agent training, but also couples rollout randomness. We study a two-action model in which independent and shared execution preserv…
Uncertainty-Aware Neural Multivariate Geostatistics
Yeseul Jeon, Aaron Scheffler, Rajarshi Guhaniyogi
We propose Deep Neural Coregionalization, a scalable framework for uncertainty-aware multivariate geostatistics. DNC models multivariate spatial effects through spatially varying l…
Deep Generative Modeling with Spatial and Network Images: An Explainable AI (XAI) Approach
Yeseul Jeon, Rajarshi Guhaniyogi, Aaron Scheffler
This article addresses the challenge of modeling the amplitude of spatially indexed low frequency fluctuations (ALFF) in resting state functional MRI as a function of cortical stru…
Interpretable Deep Neural Network for Modeling Functional Surrogates
Yeseul Jeon, Rajarshi Guhaniyogi, Aaron Scheffler +2
Developing surrogates for computer models has become increasingly important for addressing complex problems in science and engineering. This article introduces an artificial intell…